Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
A new bi-directional associative memory
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Associative memories applied to image categorization
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Low frequency response and random feature selection applied to face recognition
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Associative Memories Applied to Pattern Recognition
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Hetero-Associative Memories for Voice Signal and Image Processing
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Voice Translator Based on Associative Memories
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
A Bidirectional Hetero-Associative Memory for True-Color Patterns
Neural Processing Letters
A New Associative Model with Dynamical Synapses
Neural Processing Letters
An evolutionary feature-based visual attention model applied to face recognition
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
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In this paper we propose a view-based method for 3D object recognition based on some biological aspects of infant vision. The biological hypotheses of this method are based on the role of the response to low frequencies at early stages, and some conjectures concerning how an infant detects subtle features (stimulating points) from an object. In order to recognize an object from different images of it (different orientations from 0° to 100deg;) we make use of a dynamic associative memory (DAM). As the infant vision responds to low frequencies of the signal, a low-filter is first used to remove high frequency components from the image. Then we detect subtle features in the image by means of a random feature selection detector. At last, the DAM is fed with this information for training and recognition. To test the accuracy of the proposal we use the Columbia Object Image Library (COIL 100) database.